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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine learning based systems and methods for real time, model based diagnosis

The disclosure following relates generally to complex simulations, and fault diagnosis. In some embodiments, a component that is causing a delayed simulation time of a system is determined. A component of reduced complexity is designed, and the component of reduced complexity is used to replace the original component in the system. Fault diagnosis may then be conducted using the updated system with the reduced complexity component, thus decreasing the time taken to diagnose the fault.

Matei, Ion↗
DOE OSTI2020

A Probabilistic Model-Based Diagnostic Framework for Nuclear Engineering Systems

A fault diagnostic framework was investigated in this study for applications in thermal–hydraulic systems of nuclear power plants. The proposed framework consists of quantitative model-based diagnosis, statistical change detection and probabilistic reasoning. The use of physics-based diagnostic models provides high detection sensitivity and allows noise and measurement uncertainty to be incorporated robustly. Performance-related parametric models for each component are constructed based on first principles. Numerical model residuals are generated using the concept of analytical redundancy. Statistical change detection methods are employed to detect non-zero residuals in the presence of uncertainty. The diagnosis task is performed using Bayesian inference to detect and localize possible faults. Application to a single-phase heat exchanger for demonstration showed that the proposed probabilistic framework can provide improved results in comparison with traditional approaches while remaining less sensitive to false alarms in the presence of measurement and modeling uncertainty.

Bayesian network↗
DOE OSTI2021

A physics-based parametric regression approach for feedwater pump system diagnosis

Here in this paper, we assessed the performance of a model-based approach for fault diagnosis of a nuclear power plant feedwater pump system. Physics-based models were constructed to monitor the performance of the system components. Plant data were used for the calibration of the models and subsequently in the diagnosis of abnormal events. We considered two real-time events representing scenarios of a pump fault and component performance degradation. The two events were correctly diagnosed, and the results demonstrated the high detection sensitivity of the physics-based models. Various sensor fault scenarios were simulated to show the capability of the approach to detect and uniquely identify sensor faults. Results for a scenario in which the plant operated in flexible power mode also showed that the diagnostic approach is insensitive to changes of operating conditions, which is one of the advantages of the model-based approach using physics-based models over purely data-driven approach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Leveraging Structures in Fault Diagnosis for Lithium-Ion Battery Packs

Lithium-ion battery systems consist of a varying number of single cells, designed to meet specific application requirements for output voltage and capacity. Effective fault diagnosis in these battery systems is an essential prerequisite for ensuring their safe and reliable operation. To address this need, we introduce a novel model-based fault diagnosis approach that distinguishes itself by leveraging informative structures inherent in battery systems such as architecture, uniformity among the constituent cells, and sparsity of fault occurrences to enhance its fault diagnosis capabilities. The proposed approach formulates a moving horizon estimation (MHE) problem, incorporating such structural information to estimate different fault signals—specifically, internal short circuits, external short circuits, and voltage and current sensors faults. We conduct various simulations to evaluate the performance of the proposed approach under different fault types and magnitudes. The obtained results validate the proposed approach and promise effective fault diagnosis for battery systems.

Farakhor, Amir↗
DOE OSTI2023

Direct Bayesian inference for fault severity assessment in Digital-Twin-Based fault diagnosis

For applications in condition-based maintenance of nuclear systems, the assessment of fault severity is crucial. In this work, we developed a framework that allows for direct inference of the probability distributions of possible faults in a system. Here, we employed a model-based approach with model residuals generated from analytical redundancy relations provided by physics-based models of the system components. From real-time sensor readings, the values of the model residuals can be calculated, and the posterior probability distributions of the faults can be computed directly using the methods of Bayesian networks. From the posterior distribution of each fault, one can estimate the fault probability based on a chosen threshold and assess the severity of the fault. By eliminating the discretization and simplifications in middle steps, this approach allows us to leverage the available computational resources to provide more accurate fault probability estimates and severity assessments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring

Automating the task of fault detection and diagnosis is crucial in the effort to reduce the operation and maintenance cost in the nuclear industry. This paper describes a physics-based approach for system-level diagnosis in thermal-hydraulic systems in nuclear power plants. The inclusion of physics information allows for the creation of virtual sensors, which provide improved fault diagnosis capability. The physics information also serves to better constrain diagnostic solutions to the physical domain. As a demonstration, various test cases for fault diagnosis in a high-pressure feedwater system were considered. The use of virtual sensors allows constructing performance models for two first-point feedwater heaters which would not have been possible otherwise due to the limited sensor set. Real-time plant data provided by a utility partner were used to assess the diagnostic approach. The detection of an abnormal event immediate after a plant startup pointed to faulty behaviors in the two first-point feedwater heaters. Further, this double-blind fault diagnosis was subsequently confirmed by the plant operator. In addition, several simulated sensor fault events demonstrated the capability of our algorithms in detecting and discriminating sensor faults.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

A unified large language model–based framework for heterogeneous PV image diagnosis

With advances in imaging technologies, modern photovoltaic (PV) systems generate large volumes of heterogeneous image data, including visible, electroluminescence (EL), and infrared (IR) images. Existing PV image analysis models, particularly deep learning approaches, are typically task-specific and lack cross-modality generalization. To address this limitation, this paper proposes an open-source large language model (LLM)–based unified framework for heterogeneous PV image diagnostics. Through task-aware diagnostic prompting, the framework enables analysis of visible, EL, and IR images within a single pipeline, supporting both zero-shot and few-shot inference and binary and multiclass classification. It is compatible with state-of-the-art multimodal LLMs, including ChatGPT, Gemini, Claude, Qwen, and CLIP. The framework is evaluated on PV module condition classification (clean, soiling, snow, hail, and bird droppings) using visible images, cell crack detection using EL images, and hotspot detection using IR images. GPT-5.1 in few-shot mode achieves the best performance, with classification accuracy exceeding 97.3%. Open-source models such as Qwen and CLIP also deliver competitive results on visible images (around 90% accuracy), though their performance is more limited on EL and IR modalities. On the full ELPV dataset, the framework achieves 83.5% zero-shot accuracy, within 2.8% of the supervised CNN baseline, confirming scalability to larger benchmarks. Practical aspects such as reproducibility, response latency, and confidence estimation are systematically analyzed. The framework operates across PV image modalities without modality- or task-specific training, making it well suited as a rapid pre-screening tool to support downstream detailed diagnostics. A benchmark dataset of diverse labeled PV images is also released.

Li, Baojie↗
DOE OSTI2023

Dynamic model-based feature extraction for fault detection and diagnosis of a supermarket refrigeration system

With the increasing concerns over climate change and carbon emissions, fault detection and diagnostics (FDD) of low–global warming potential (GWP) refrigerant supermarket refrigeration systems has gained great attention from academic and industrial sectors. Various FDD approaches have been developed to detect, identify, and diagnose faults to save energy, improve food quality, and protect the environment. Here, to mitigate the difficulty of collecting high-quality steady-state operational data in field operations faced by most model-based FDD methods, this study developed dynamic models of a low–GWP refrigerant (CO 2 ) supermarket refrigeration system. The model accuracy was validated using manufacturer data and experimental data. Simulations were conducted to predict the system dynamic response under two common operational faults—evaporator air path blockage fault and the display case door open fault—to identify fault patterns and define key dynamic behavior indexes for supporting FDD algorithm development.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Projected increase of the East Asian summer monsoon ( Meiyu ) in Taiwan by climate models with variable performance

Abstract The active phase of the East Asian summer monsoon (EASM) in Taiwan during May and June, known as Meiyu , produces substantial precipitation for water uses in all sectors of society. Following a companion study that analysed the historical increase in the Meiyu precipitation, the present study conducted model evaluation and diagnosis based on the EASM lifecycle over Taiwan. Higher and lower skill groups were identified from 17 Couple Model Intercomparison Project Phase 5 (CMIP5) models, with five models in each group. Despite the difference in model performance, both groups projected a substantial increase in the Meiyu precipitation over Taiwan. In the higher skill group, weak circulation changes and reduced low‐level convergence point to a synoptically unfavourable condition for precipitation. In the lower skill group, intensified low‐level southwesterly winds associated with a deepened upper level trough enhance moisture pooling. Thus, the projected increase in Meiyu precipitation will likely occur through the combined effects of (1) the extension of a strengthened North Pacific anticyclone enhancing southwesterlies; and (2) more systematically, the Clausius–Clapeyron relationship that increases precipitation intensity in a warmer climate. The overall increase in the Meiyu precipitation projected by climate models of variable performance supports the observed tendency toward more intense rainfall in Taiwan and puts its early June 2017 extreme precipitation events into perspective.

Tung, Yu‐Shiang↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Stochastic Fault Detection

This entry describes the state-of-the-art and future perspectives on stochastic fault detection, namely, stochastic fault detection and diagnosis (FDD). Both model-based and data-driven FDD methods for stochastic signals and systems have been included, where the use of hypothesis testing, Kalman filtering, system estimation, principal component analysis (PCA), and stochastic distribution control has been discussed for the construction of effective FDD algorithms. Indeed, stochastic FDD constitute an important and integrated part in developing fault-tolerant controls (FTC) for guaranteed safe operation of control systems, of which increased penetration of random factors is inevitable nowadays.

Wang, Aiping↗
DOE OSTI2022

A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems

We report faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Development and Validation of Home Comfort System for Total Performance Deficiency/Fault Detection and Optimal Comfort Control

In this project, we developed and tested a learning-based home thermal model that facilitates the operation of a model predictive control (MPC)-based optimization agent and an automated fault detection and diagnosis (AFDD) agent. The home thermal model was constructed using a two-node resistor-capacitor model. Moreover, two accompanying parameter identification methods were introduced, least-squares and optimization. Based on the home thermal model, the MPC-based optimization agent was developed to optimize residential HVAC operation. Using two FDD methods, the AFDD agent was constructed to detect and diagnose two prevalent residential AC faults, airflow reduction and refrigerant undercharge. The home thermal model, along with the MPC-based optimization agent and AFDD agent, were tested at the Norman Test House, Miami Test House, Pacific Northwest National Laboratory (PNNL) Test House A, and PNNL Test House B. Finally, they were also field tested in nine demonstration homes with real occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Effect of vaccination on the case fatality rate for COVID-19 infections 2020–2021: multivariate modelling of data from the US Department of Veterans Affairs

Objectives: To evaluate the benefits of vaccination on the case fatality rate (CFR) for COVID-19 infections. Design, setting and participants: The US Department of Veterans Affairs has 130 medical centres. We created multivariate models from these data—339 772 patients with COVID-19—as of 30 September 2021. Outcome measures: The primary outcome for all models was death within 60 days of the diagnosis. Logistic regression was used to derive adjusted ORs for vaccination and infection with Delta versus earlier variants. Models were adjusted for confounding factors, including demographics, comorbidity indices and novel parameters representing prior diagnoses, vital signs/baseline laboratory tests and outpatient treatments. Patients with a Delta infection were divided into eight cohorts based on the time from vaccination to diagnosis. A common model was used to estimate the odds of death associated with vaccination for each cohort relative to that of unvaccinated patients. Results: 9.1% of subjects were vaccinated. 21.5% had the Delta variant. 18 120 patients (5.33%) died within 60 days of their diagnoses. The adjusted OR for a Delta infection was 1.87±0.05, which corresponds to a relative risk (RR) of 1.78. The overall adjusted OR for prior vaccination was 0.280±0.011 corresponding to an RR of 0.291. Raw CFR rose steadily after 10–14 weeks. The OR for vaccination remained stable for 10–34 weeks. Conclusions: Our CFR model controls for the severity of confounding factors and priority of vaccination, rather than solely using the presence of comorbidities. Our results confirm that Delta was more lethal than earlier variants and that vaccination is an effective means of preventing death. After adjusting for major selection biases, we found no evidence that the benefits of vaccination on CFR declined over 34 weeks. We suggest that this model can be used to evaluate vaccines designed for emerging variants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

EXPLAINABLE AND TRUSTWORTHY DIAGNOSTICS ACHIEVABLE THROUGH PROCESS-BASED AUTOMATED REASONING

An approach has been developed that incorporates domain knowledge to obtain a more explainable and trustworthy equipment health monitoring diagnosis than might otherwise be obtained from a purely data-driven method. Physics-based models serve to constrain the realizable solution space and render a more trusted diagnosis. An automated reasoning algorithm performs backward chaining to infer a diagnosis that is consistent with logic statements that have been evaluated as true. This diagnosis is made explainable to an operator by providing the forward chaining path that elucidates for inspection and validity testing those truths implied by the diagnosis.

automated reasoning↗
DOE OSTI2020

Predictive Modeling and Diagnostic Monitoring of Extreme Science Workflows (Final Report)

This proposal addresses a critical issue of performance prediction identified in the report from the ASCR \Computational Modeling of Big Networks (COMBINE)" workshop: "end-to-end performance is not predictable due to a variety of factors. Even when some performance forecasts or predictions can be made, they often cannot explain the reasons why some predictions fail." We will develop new analytical models to predict the end-to-end performance of scientific workflows on DOE computing infrastructures, and use simulations and experimentation to validate and refine these models, as well as to pinpoint the sources of model inaccuracy. We will also use these models to help diagnose application and infrastructure problems, and to adapt the system based on this diagnosis. This section provides background in the areas relevant to the proposed work. RPI’s specific tasks within the Panorama project are as follows: (1) Develop Aspen-Simulation interface for Workflow Model Driven Simulation. (2) Validate manual performance models of two target workflow scenarios with empirical measurement and simulation. (3) Extend ROSS-Aspen API to simulate workflow descriptions when required. (4) Validate Aspen performance models of two target workflow scenarios with automatic performance model empirical measurement and simulation. (5) Design and implement final system to automatically generate Aspen performance models from workflow descriptions (including methods to compensate for limitations of Aspen analytical models). (6) Validate improved Aspen performance models with target workflow on production infrastructure. To date, all the project milestones assigned to us where reached within the best of our abilities over the course of the project performance period. Below describes the key outcome from our collaborative research in a system named, Durango .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multi-fidelity physics-informed machine learning for probabilistic damage diagnosis

Machine learning (ML) models are gaining popularity in structural health monitoring (SHM) because of their ability to learn the complex relationship between damage and sensor data. However, the lack of sufficient experimental data for structures with different degrees of damage is a key problem in training ML models for SHM. This problem can be alleviated by using physics-based models to generate the required training data to build physics-informed ML (PIML) models for SHM. However, it takes significant computational effort to perform enough high-fidelity simulations of the diagnostic test. It is thus desirable to know whether the available computational resource budget should be expended on numerous low-fidelity physics simulations, or a small number of high-fidelity simulations, or their combination. In this paper, we investigate this aspect of generating adequate training data for PIML, by constructing multi-fidelity PIML models. We evaluate the performance of several PIML models, trained with different amounts of low-fidelity and high-fidelity data, in locating hidden cracks in concrete structures using a nonlinear dynamics-based diagnosis technique. Here, we find that high-fidelity physics simulations that do not cover the (test and damage) parameter space do not improve the performance of diagnostic PIML models built using data from many low-fidelity physics simulations.

42 ENGINEERING↗